Cargando…

Clustering of Unhealthy Behaviors: Protocol for a Multiple Behavior Analysis of Data From the Canadian Longitudinal Study on Aging

BACKGROUND: Health behaviors such as physical inactivity, unhealthy eating, smoking tobacco, and alcohol use are leading risk factors for noncommunicable chronic diseases and play a central role in limiting health and life satisfaction. To date, however, health behaviors tend to be considered separa...

Descripción completa

Detalles Bibliográficos
Autores principales: van Allen, Zack, Bacon, Simon L, Bernard, Paquito, Brown, Heather, Desroches, Sophie, Kastner, Monika, Lavoie, Kim, Marques, Marta, McCleary, Nicola, Straus, Sharon, Taljaard, Monica, Thavorn, Kednapa, Tomasone, Jennifer R, Presseau, Justin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8235290/
https://www.ncbi.nlm.nih.gov/pubmed/34114962
http://dx.doi.org/10.2196/24887
_version_ 1783714282022633472
author van Allen, Zack
Bacon, Simon L
Bernard, Paquito
Brown, Heather
Desroches, Sophie
Kastner, Monika
Lavoie, Kim
Marques, Marta
McCleary, Nicola
Straus, Sharon
Taljaard, Monica
Thavorn, Kednapa
Tomasone, Jennifer R
Presseau, Justin
author_facet van Allen, Zack
Bacon, Simon L
Bernard, Paquito
Brown, Heather
Desroches, Sophie
Kastner, Monika
Lavoie, Kim
Marques, Marta
McCleary, Nicola
Straus, Sharon
Taljaard, Monica
Thavorn, Kednapa
Tomasone, Jennifer R
Presseau, Justin
author_sort van Allen, Zack
collection PubMed
description BACKGROUND: Health behaviors such as physical inactivity, unhealthy eating, smoking tobacco, and alcohol use are leading risk factors for noncommunicable chronic diseases and play a central role in limiting health and life satisfaction. To date, however, health behaviors tend to be considered separately from one another, resulting in guidelines and interventions for healthy aging siloed by specific behaviors and often focused only on a given health behavior without considering the co-occurrence of family, social, work, and other behaviors of everyday life. OBJECTIVE: The aim of this study is to understand how behaviors cluster and how such clusters are associated with physical and mental health, life satisfaction, and health care utilization may provide opportunities to leverage this co-occurrence to develop and evaluate interventions to promote multiple health behavior changes. METHODS: Using cross-sectional baseline data from the Canadian Longitudinal Study on Aging, we will perform a predefined set of exploratory and hypothesis-generating analyses to examine the co-occurrence of health and everyday life behaviors. We will use agglomerative hierarchical cluster analysis to cluster individuals based on their behavioral tendencies. Multinomial logistic regression will then be used to model the relationships between clusters and demographic indicators, health care utilization, and general health and life satisfaction, and assess whether sex and age moderate these relationships. In addition, we will conduct network community detection analysis using the clique percolation algorithm to detect overlapping communities of behaviors based on the strength of relationships between variables. RESULTS: Baseline data for the Canadian Longitudinal Study on Aging were collected from 51,338 participants aged between 45 and 85 years. Data were collected between 2010 and 2015. Secondary data analysis for this project was approved by the Ottawa Health Science Network Research Ethics Board (protocol ID #20190506-01H). CONCLUSIONS: This study will help to inform the development of interventions tailored to subpopulations of adults (eg, physically inactive smokers) defined by the multiple behaviors that describe their everyday life experiences. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/24887
format Online
Article
Text
id pubmed-8235290
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher JMIR Publications
record_format MEDLINE/PubMed
spelling pubmed-82352902021-07-02 Clustering of Unhealthy Behaviors: Protocol for a Multiple Behavior Analysis of Data From the Canadian Longitudinal Study on Aging van Allen, Zack Bacon, Simon L Bernard, Paquito Brown, Heather Desroches, Sophie Kastner, Monika Lavoie, Kim Marques, Marta McCleary, Nicola Straus, Sharon Taljaard, Monica Thavorn, Kednapa Tomasone, Jennifer R Presseau, Justin JMIR Res Protoc Proposal BACKGROUND: Health behaviors such as physical inactivity, unhealthy eating, smoking tobacco, and alcohol use are leading risk factors for noncommunicable chronic diseases and play a central role in limiting health and life satisfaction. To date, however, health behaviors tend to be considered separately from one another, resulting in guidelines and interventions for healthy aging siloed by specific behaviors and often focused only on a given health behavior without considering the co-occurrence of family, social, work, and other behaviors of everyday life. OBJECTIVE: The aim of this study is to understand how behaviors cluster and how such clusters are associated with physical and mental health, life satisfaction, and health care utilization may provide opportunities to leverage this co-occurrence to develop and evaluate interventions to promote multiple health behavior changes. METHODS: Using cross-sectional baseline data from the Canadian Longitudinal Study on Aging, we will perform a predefined set of exploratory and hypothesis-generating analyses to examine the co-occurrence of health and everyday life behaviors. We will use agglomerative hierarchical cluster analysis to cluster individuals based on their behavioral tendencies. Multinomial logistic regression will then be used to model the relationships between clusters and demographic indicators, health care utilization, and general health and life satisfaction, and assess whether sex and age moderate these relationships. In addition, we will conduct network community detection analysis using the clique percolation algorithm to detect overlapping communities of behaviors based on the strength of relationships between variables. RESULTS: Baseline data for the Canadian Longitudinal Study on Aging were collected from 51,338 participants aged between 45 and 85 years. Data were collected between 2010 and 2015. Secondary data analysis for this project was approved by the Ottawa Health Science Network Research Ethics Board (protocol ID #20190506-01H). CONCLUSIONS: This study will help to inform the development of interventions tailored to subpopulations of adults (eg, physically inactive smokers) defined by the multiple behaviors that describe their everyday life experiences. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/24887 JMIR Publications 2021-06-11 /pmc/articles/PMC8235290/ /pubmed/34114962 http://dx.doi.org/10.2196/24887 Text en ©Zack van Allen, Simon L Bacon, Paquito Bernard, Heather Brown, Sophie Desroches, Monika Kastner, Kim Lavoie, Marta Marques, Nicola McCleary, Sharon Straus, Monica Taljaard, Kednapa Thavorn, Jennifer R Tomasone, Justin Presseau. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 11.06.2021. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Research Protocols, is properly cited. The complete bibliographic information, a link to the original publication on https://www.researchprotocols.org, as well as this copyright and license information must be included.
spellingShingle Proposal
van Allen, Zack
Bacon, Simon L
Bernard, Paquito
Brown, Heather
Desroches, Sophie
Kastner, Monika
Lavoie, Kim
Marques, Marta
McCleary, Nicola
Straus, Sharon
Taljaard, Monica
Thavorn, Kednapa
Tomasone, Jennifer R
Presseau, Justin
Clustering of Unhealthy Behaviors: Protocol for a Multiple Behavior Analysis of Data From the Canadian Longitudinal Study on Aging
title Clustering of Unhealthy Behaviors: Protocol for a Multiple Behavior Analysis of Data From the Canadian Longitudinal Study on Aging
title_full Clustering of Unhealthy Behaviors: Protocol for a Multiple Behavior Analysis of Data From the Canadian Longitudinal Study on Aging
title_fullStr Clustering of Unhealthy Behaviors: Protocol for a Multiple Behavior Analysis of Data From the Canadian Longitudinal Study on Aging
title_full_unstemmed Clustering of Unhealthy Behaviors: Protocol for a Multiple Behavior Analysis of Data From the Canadian Longitudinal Study on Aging
title_short Clustering of Unhealthy Behaviors: Protocol for a Multiple Behavior Analysis of Data From the Canadian Longitudinal Study on Aging
title_sort clustering of unhealthy behaviors: protocol for a multiple behavior analysis of data from the canadian longitudinal study on aging
topic Proposal
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8235290/
https://www.ncbi.nlm.nih.gov/pubmed/34114962
http://dx.doi.org/10.2196/24887
work_keys_str_mv AT vanallenzack clusteringofunhealthybehaviorsprotocolforamultiplebehavioranalysisofdatafromthecanadianlongitudinalstudyonaging
AT baconsimonl clusteringofunhealthybehaviorsprotocolforamultiplebehavioranalysisofdatafromthecanadianlongitudinalstudyonaging
AT bernardpaquito clusteringofunhealthybehaviorsprotocolforamultiplebehavioranalysisofdatafromthecanadianlongitudinalstudyonaging
AT brownheather clusteringofunhealthybehaviorsprotocolforamultiplebehavioranalysisofdatafromthecanadianlongitudinalstudyonaging
AT desrochessophie clusteringofunhealthybehaviorsprotocolforamultiplebehavioranalysisofdatafromthecanadianlongitudinalstudyonaging
AT kastnermonika clusteringofunhealthybehaviorsprotocolforamultiplebehavioranalysisofdatafromthecanadianlongitudinalstudyonaging
AT lavoiekim clusteringofunhealthybehaviorsprotocolforamultiplebehavioranalysisofdatafromthecanadianlongitudinalstudyonaging
AT marquesmarta clusteringofunhealthybehaviorsprotocolforamultiplebehavioranalysisofdatafromthecanadianlongitudinalstudyonaging
AT mcclearynicola clusteringofunhealthybehaviorsprotocolforamultiplebehavioranalysisofdatafromthecanadianlongitudinalstudyonaging
AT straussharon clusteringofunhealthybehaviorsprotocolforamultiplebehavioranalysisofdatafromthecanadianlongitudinalstudyonaging
AT taljaardmonica clusteringofunhealthybehaviorsprotocolforamultiplebehavioranalysisofdatafromthecanadianlongitudinalstudyonaging
AT thavornkednapa clusteringofunhealthybehaviorsprotocolforamultiplebehavioranalysisofdatafromthecanadianlongitudinalstudyonaging
AT tomasonejenniferr clusteringofunhealthybehaviorsprotocolforamultiplebehavioranalysisofdatafromthecanadianlongitudinalstudyonaging
AT presseaujustin clusteringofunhealthybehaviorsprotocolforamultiplebehavioranalysisofdatafromthecanadianlongitudinalstudyonaging